public string utterWord(Agent speaker)
{
Dictionary <string, double> vocabulary = speaker.getVocabulary().getVocabulary();
if (EALoop.RandomInt(0, 100) <= 40)
{
return(newWord());
}
if (vocabulary.Count == 0)
{
return(newWord());
}
double sum = 0;
var sortedDict = from entry in vocabulary orderby entry.Value descending select entry;
foreach (var i in sortedDict)
{
sum += i.Value;
}
double rnd = EALoop.RandomDouble();
double prob = 0;
foreach (var i in sortedDict)
{
prob += i.Value / sum;
if (rnd <= prob)
{
return(i.Key);
}
}
return(vocabulary.ElementAt(EALoop.RandomInt(0, vocabulary.Count)).Key);
}
public Genome()
{
genomeNormalised = new List <double>();
genomeValues = new List <double>();
for (int i = 0; i < 10; i++)
{
genomeValues.Add(0.0);
}
int n = 20;
genomeValues[0] = EALoop.RandomInt(0, 100); // Random value
genomeValues[1] = (genomeValues[0] * (1 + (System.Convert.ToDouble(EALoop.RandomInt(0, n)) / 100))); // Close to value #0
genomeValues[5] = (100 - genomeValues[0]) * (1 + (System.Convert.ToDouble(EALoop.RandomInt(0, n)) / 100)); // Opposite of #0
genomeValues[6] = genomeValues[5] * (1 + (System.Convert.ToDouble(EALoop.RandomInt(0, n)) / 100)); // Close to value #5
genomeValues[7] = genomeValues[6] * (1 + (System.Convert.ToDouble(EALoop.RandomInt(0, n)) / 100)); // Close to value #6
genomeValues[3] = EALoop.RandomInt(0, 100); // Random value
genomeValues[4] = genomeValues[3] * (1 + (System.Convert.ToDouble(EALoop.RandomInt(0, n)) / 100)); // Close to value #3
genomeValues[8] = (100 - genomeValues[4]) * (1 + (System.Convert.ToDouble(EALoop.RandomInt(0, n)) / 100)); // Opposite of #4
genomeValues[9] = genomeValues[8] * (1 + (System.Convert.ToDouble(EALoop.RandomInt(0, n)) / 100)); // Close to value 8
genomeValues[2] = ((genomeValues[1] + genomeValues[3]) / 2) * (1 + (System.Convert.ToDouble(EALoop.RandomInt(0, n / 2)) / 100)); // Close to #3 and #1
//-- Normalise genome --//
genomeNormalised = normalise(genomeValues);
}
public void mutate(double p)
{
if (EALoop.RandomDouble() <= p)
{
//double mutateRate = (EALoop.RandomInt(0,4) - 2) / 10; // Blir et tall mellom -0.2 og 0.2
//if (mutateRate == 0.0) { mutateRate = 1.01; }
//int index = EALoop.RandomInt(0,genomeValues.Count);
genomeValues[EALoop.RandomInt(0, genomeValues.Count)] = EALoop.RandomInt(0, 100);
genomeNormalised = normalise(genomeValues);
}
}
private string newWord()
{
int length = EALoop.RandomInt(1, 5);
string word = "";
for (int i = 0; i < length; i++)
{
int n = EALoop.RandomInt(0, 26);
word += newLetter(n);
}
return(word);
}
public Agent selectSpeaker(List <Agent> pop)
{
double sum = 0;
foreach (Agent a in pop)
{
sum += a.getFitness();
}
double rnd = EALoop.RandomDouble();
double n = 0;
foreach (Agent a in pop)
{
n += a.getFitness() / sum;
if (rnd <= n)
{
return(a);
}
}
return(pop[EALoop.RandomInt(0, pop.Count)]);
}
public Agent selectListener(Agent agent, SocialNetwork net, List <Agent> population)
{
var genome = agent.getGenome().getValuesGenome();
double P_extrovert = ((genome[3] + (100 - genome[6]) / 200) * C) / 100;
Dictionary <Agent, double> connections = net.getAgentsConnections(agent);
//if ((EALoop.RandomDouble() <= P_extrovert && agent.getZ() < maxExtroConv) || net.getAgentsConnections(agent) == null)
if (EALoop.RandomDouble() <= P_extrovert)
{
// Extrovert
//System.Console.WriteLine("EXTROVERT");
Agent listener = population[EALoop.RandomInt(0, population.Count)];
while (listener == agent)
{
listener = population[EALoop.RandomInt(0, population.Count)];
}
agent.incrementZ();
return(listener);
}
// Introvert
double sum = 0;
foreach (var friend in connections)
{
sum += friend.Value;
}
double random = EALoop.RandomDouble();
double to = 0;
foreach (var friend in connections)
{
to += friend.Value / sum;
if (random <= to)
{
return(friend.Key);
}
}
return(null);
}